Updated Fielding Projections

As of this morning, we’ve updated our fielding projections. There are two major changes in the way we’re cooking this year’s fielding projections. First, these are now based on more data: 5 past years and the current season (in-season, that is) of UZR data instead of merely 2 past years of UZR’s and the current season. Second, they are regressed towards Tom Tango’s Fan Scouting Reports instead of towards zero. This ends up creating somewhat more aggressive projections. Andrelton Simmons leads the way and is projected to be 21 runs better than the average shortstop. The biggest gainer from the old system to the new system is Brett Gardner who goes from a projection of merely +1 all the way up to +14 (the 4th best UZR projection after Simmons, Machado and Arenado). Check out the table below to see how fielders fared under each system.

Comments (11)

MPMarch 8, 2014 at 7:10 am

Does Steamer either explicitly or implicitly use strength of schedule as a factor in its projections? The question occurred to me after reading this: http://www.fangraphs.com/blogs/2014-strengths-of-schedule-projected/. I suppose things would get recursive in a hurry since the SOS calcs. were done using Steamer as an input, but wondering if your team adjustments already include some adjustments for historical schedule disparities?

Thinking a little more about it, I suppose by definition you are already including some weighted average of past schedule difficulty when you make your projections. That is, a pitcher in the AL East will usually have worse peripherals than his “true skill” — even in a hypothetically neutral park — simply because the AL East plays so tough on average. So, in theory, any projection system ignoring SOS will tend to under-rate players moving from a reliably tough division (over past 3 years) to an easier division, and vice versa. For players remaining in the same division (i.e., most players), the current system of not adjusting for schedule effects is probably fine.

The ideal methodology would probably be to calc. a player’s weighted avg. past schedule difficulty (across multiple teams, if necessary), compare to his projected current season SOS, and adjust his projection accordingly.

What would *really* be interesting (and challenging) would be to calc. a player’s past SOS by looking at the actual batters or pitchers he faced (weighted by PA of course), to come up with schedule-neutral stats, which would be used to arrive at a schedule-neutral stat projection. Then apply current-season projected SOS to get the actual projection. I honestly feel like that might be the next big step for projection systems.

That would be cool. Definitely something to work towards in future years. The first thing we want to neutralize for, I think, is catcher framer skill, but we have some work to do in order to get there.

BenMarch 8, 2014 at 2:37 pm

In terms of placing proper value on pitching rate stats in my league (K/9, K/BB, ERA, WHIP, BAA), I’m trying to weight my Z-values for those by innings pitched, so that somebody with more IP gets more credit, since he affects more games. My formula for this has been to multiply the initial Z-value by (Individual IP/201), since 201 was the maximum IP in the Steamer projections (Wainwright). For example,

However, when I do this, top relievers like Kimbrel/Chapman/Jansen take massive hits in value; Kimbrel goes from being valued just outside the top 10 overall to being behind the likes of Hyun-Jin Ryu, Ian Kennedy and Matt Garza, while Jansen slips behind Scott Kazmir. I feel like this is too big a drop, but I’m not sure how to mitigate this without overbalancing. Thoughts?

What sample are you getting the mean and standard deviation from for your z-scores?

If you’re only using the top 100-150 pitchers or so to calculate z-scores, then elite relievers like Kimbrel should have astronomical z-scores on ERA* (z = 3.55 in my data; 1.86 projected ERA). Elite starters like Strasburg are much lower (z = 1.03; 2.94 projected ERA). So, even when you weight the standardized score by IP (and I just multiply directly), Kimbrel with his 70 or so IP (70 x 3.55 = 248.5) still comes out ahead of Strasburg and his 180 IP (180 x 1.03 = 185.4). I then recalculate z-scores for the weighted ratios so they can be aggregated with other categories.

The problem isn’t that Kimbrel’s ERA value isn’t still high; it’s that his OVERALL value (sum of all categories) dips dramatically. My league has these pitching categories:

IP, W, ERA, SO, SV, HD, QS, BAA, WHIP, K/9, K/BB.

As you can see, with five rate stats, the impact over more IP is huge. Am I twisting data to fit conclusions, or am I right to think Kimbrel is significantly more valuable in my league than Ryu/Kennedy/Garza?

The reason SO is a starter category is simply due to having more opportunities to striking people out, so top flight starters have necessarily better SO numbers than top flight closers.

The thing about Kimbrel (and other top flight closers) though is that their SO numbers are actually competitive with mid tier and even 2nd tier pitching, logging 100+Ks over the season. Kimbrel is also dominant in the neutral categories, performing better than most starters, even when accounting for lower IP contribution. This is why it feels like Kimbrel should at least be valued within the top pitching tiers, because his contribution feels strong.

Using the maximum IP to modify rate stats will effectively give starters an extra category worth of weight, since closers will likely fall below the mean. What I’d suggest is limiting your population of available players for scoring and then creating a mean of IP, and use that to normalize your rate stats. The reason you have to limit the population is that there is a large number of tracked pitchers with very low IP, so it ends up skewing the mean. A good way to limit the population is to use the population provided by your fantasy league provider. This type of normalization will tend to exaggerate extreme outliers a bit, but you can probably make a case that the best of the best should be exaggerated.

It’s also worth noting that if you’re using your Z-Scores to effectively measure pitchers against batters, you should apply the same methodology to batting rate scores (AVG, OBP, SLG, OPS), else batters will be flatter with respect to the pitchers.

Thanks. We don’t do any of those 3 right now, I’m afraid. We were thinking about adding QS and holds in the future. I’d never even considered FPCT though — I’m actually hoping that disappears from the stat landscape.